Completion of Global Ionospheric TEC Maps Using a Deep Learning Approach

Abstract

Total electron content (TEC) is an important parameter that describes the features of the ionosphere. The International GNSS Service (IGS) has been providing IGS Global TEC maps using analysis algorithms. However, collecting the completed data is difficult because of the lack of ground receivers, and the processing to obtain the completed IGS TEC maps is time‐consuming. The fast development of deep learning brings an effective way to solve these problems. Among the various deep learning methods, the generative adversarial network (GAN) exhibits great potential in recovering missing data. In this paper, we fill the missing data of the global IGS TEC maps using pix2pixhd, which is a novel deep learning method based on GAN. Differing from the traditional GAN, pix2pixhd has two generators and three discriminators. The network enhances the ability of our model to complete images with large‐scale missing areas. The result demonstrates that our model generates the ionospheric peak structures at low latitudes well, while behaving badly (the average correlation coefficient: 0.6857) around the edge of the ionospheric peak region. Comparing different scales of the missing data areas, our model has the best performance with 0%–15% missing data. With the large scale of missing data areas (30%–45% and >45%), the performance is still satisfactory. In addition, the completion effect of our model is slightly affected by geomagnetic and solar activity. Our work demonstrates a new possibility for the application of deep learning to a broader field of geosciences, particularly for problems of missing observational data.

A Low‐Latitude Three‐Dimensional Ionospheric Electron Density Model Based on Radio Occultation Data Using Artificial Neural Networks With Prior Knowledge

“…(2021) proposed the spectrally normalized patch GAN for TEC estimation, which reduces the Root Mean Square Error (RMSE) by more than 30% compared to DCGAN‐PB. Yang, Fang, and Liu (2022) constructed an ionospheric TEC model using a novel deep learning method, pix2pixhd. The model can fill the large‐scale missing data of the global IGS TEC map.…”

Two Hours Ahead Prediction of the TEC over China Using a Deep Learning Method

“…Isola et al proposed Conditional Generative Adversarial Networks (CGANs) to solve the image-to-image translation problem [22], and they released the pix2pix software. Subsequently, Wang et al further improved pix2pix and released the pix2pixhd software for outputting high-quality images [23] generated high-resolution images of the sun using pix2pixhd [24] used pix2pixhd to predict the global TEC map successfully, and their model outperformed the IRI model [25] completed the global ionospheric TEC maps with missing areas using pix2pixhd to solve the problem of missing observational data [26].…”